In Fourier transform, we take some signals in space or time and write them into their frequency components. Gabor transform is the special case of the short-time Fourier transform used to extract the sinusoidal frequency and phase content of a signal in its particular section. We use the Gabor transform to compute the spectrogram. The mathematics behind the spectrogram is based on the Gabor transform. The image below can give us the visualization of these components. AmplitudeĪmplitude can be defined as the greatest distance travelled by a moving body in a periodic motion in a single time unit or the highest distance of the wave on dips down or rising from its flat surface. Mathematically, frequency is the number of waves passing through a fixed point in a single time unit or the number of cycles performed by a body in a single time when it is in a periodic motion. Next in the article, we will have a general definition of both of them so that we won’t get confused about the terms we will use later in the article. So the amplitude and the frequency of the signal are the two main components of any spectrogram. Finally, it gives you an overview of the signal where it explains how the strength of the signal is distributed in different frequencies. Also, it can be on different colors where the density of colors can be considered the signal’s strength. Therefore, measuring the frequency and amplitude of the signals can be considered the main motive of the spectrogram.įor visualising signals into an image, we use a spectrogram that plots the time in the x-axis and frequency in the y-axis and, for more detailed information, amplitude in the z-axis.
![music spectrograph frequency music spectrograph frequency](https://thumbs.dreamstime.com/z/sound-audio-wave-equalizer-music-frequency-color-spectrum-vector-flat-design-191849178.jpg)
Audio files, sound waves, and magnetic waves are the most common examples of this kind of data all of them provide signal information in the form of data.
MUSIC SPECTROGRAPH FREQUENCY SERIES
Still, when it comes to analysis, a series evolving with time, the spectrogram is the most common tool we frequently use to analyze this kind of data. In this modern data science scenario, there are many kinds of data required to analyze, and various analysis algorithms help us view the data better or understand the data.